chore: update models to use slice/chunk/chunksections (#12934)

* use slice/chunks

* bert

* llama4

* gemma3n

* gptoss

* mistral3

* qwen3vl

* qwen25vl

* deepseek2

* remove unused ops

convert/convert_gptoss.go

@@ -110,9 +110,12 @@ func (m *gptossModel) Tensors(ts []Tensor) []*ggml.Tensor {
 
 	for name, mxfp4 := range mxfp4s {
 		dims := mxfp4.blocks.Shape()
+		if !strings.HasSuffix(name, ".weight") {
+			name = name + ".weight"
+		}
 		if strings.Contains(name, "ffn_down_exps") {
 			out = append(out, &ggml.Tensor{
-				Name:     name + ".weight",
+				Name:     name,
 				Kind:     uint32(ggml.TensorTypeMXFP4),
 				Shape:    []uint64{dims[0], dims[1], dims[2] * dims[3] * 2},
 				WriterTo: mxfp4,
@@ -121,12 +124,12 @@ func (m *gptossModel) Tensors(ts []Tensor) []*ggml.Tensor {
 			// gate_up_exps is interleaved, need to split into gate_exps and up_exps
 			// e.g. gate_exps, up_exps = gate_up_exps[:, 0::2, ...], gate_up_exps[:, 1::2, ...]
 			out = append(out, &ggml.Tensor{
-				Name:     strings.Replace(name, "gate_up", "gate", 1) + ".weight",
+				Name:     strings.Replace(name, "gate_up", "gate", 1),
 				Kind:     uint32(ggml.TensorTypeMXFP4),
 				Shape:    []uint64{dims[0], dims[1] / 2, dims[2] * dims[3] * 2},
 				WriterTo: mxfp4.slice(1, 0, int(dims[1]), 2),
 			}, &ggml.Tensor{
-				Name:     strings.Replace(name, "gate_up", "up", 1) + ".weight",
+				Name:     strings.Replace(name, "gate_up", "up", 1),
 				Kind:     uint32(ggml.TensorTypeMXFP4),
 				Shape:    []uint64{dims[0], dims[1] / 2, dims[2] * dims[3] * 2},
 				WriterTo: mxfp4.slice(1, 1, int(dims[1]), 2),

ml/backend.go

@@ -146,7 +146,6 @@ type Tensor interface {
 	FromFloats([]float32)
 	FromInts([]int32)
 
-	Neg(ctx Context) Tensor
 	Add(ctx Context, t2 Tensor) Tensor
 	Sub(ctx Context, t2 Tensor) Tensor
 	Mul(ctx Context, t2 Tensor) Tensor
@@ -185,7 +184,6 @@ type Tensor interface {
 	View(ctx Context, offset int, shape ...int) Tensor
 	Permute(ctx Context, shape ...int) Tensor
 	Contiguous(ctx Context, shape ...int) Tensor
-	Set(ctx Context, t2 Tensor, offset int, strides ...int) Tensor
 
 	Pad(ctx Context, shape ...int) Tensor
 
@@ -209,7 +207,6 @@ type Tensor interface {
 	Stddev(ctx Context) Tensor
 	Sqr(ctx Context) Tensor
 	Sqrt(ctx Context) Tensor
-	Clamp(ctx Context, min, max float32) Tensor
 }
 
 // ScaledDotProductAttention implements a fused attention

ml/backend/ggml/ggml.go

@@ -1137,13 +1137,6 @@ func (t *Tensor) Cast(ctx ml.Context, dtype ml.DType) ml.Tensor {
 	}
 }
 
-func (t *Tensor) Neg(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_neg(ctx.(*Context).ctx, t.t),
-	}
-}
-
 func (t *Tensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	return &Tensor{
 		b: t.b,
@@ -1632,20 +1625,6 @@ func (t *Tensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
 	}
 }
 
-func (t *Tensor) Set(ctx ml.Context, t2 ml.Tensor, offset int, strides ...int) ml.Tensor {
-	var tt *C.struct_ggml_tensor
-	switch len(strides) {
-	case 0:
-		tt = C.ggml_set_1d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.size_t(offset))
-	case 1:
-		tt = C.ggml_set_2d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.size_t(offset), C.size_t(strides[0]))
-	default:
-		panic("unsupported number of dimensions")
-	}
-
-	return &Tensor{b: t.b, t: tt}
-}
-
 func (t *Tensor) ScaledDotProductAttention(ctx ml.Context, key, value, mask, sinks ml.Tensor, scale float64) ml.Tensor {
 	var kqMask *C.struct_ggml_tensor
 	if mask != nil {
@@ -1732,13 +1711,6 @@ func (t *Tensor) Sqrt(ctx ml.Context) ml.Tensor {
 	}
 }
 
-func (t *Tensor) Clamp(ctx ml.Context, min, max float32) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_clamp(ctx.(*Context).ctx, t.t, C.float(min), C.float(max)),
-	}
-}
-
 // Slice returns a view of the tensor sliced along dim from low to high in step steps.
 // Slice panics if the dimension is invalid or the slice parameters are out of range.
 // If dim=0 and step>1, the tensor is a copy rather than a view to ensure proper shape.

ml/nn/pooling/pooling.go

@@ -32,10 +32,9 @@ func (t Type) Forward(ctx ml.Context, hiddenStates ml.Tensor) ml.Tensor {
 		hiddenStates = hiddenStates.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx).Mean(ctx)
 		return hiddenStates.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
 	case TypeCLS:
-		return hiddenStates.View(ctx, 0, hiddenStates.Dim(0))
+		return hiddenStates.Slice(ctx, 1, 0, 1, 1)
 	case TypeLast:
-		hiddenStates = hiddenStates.View(ctx, (hiddenStates.Dim(1)-1)*hiddenStates.Stride(1), hiddenStates.Dim(0))
-		return hiddenStates
+		return hiddenStates.Slice(ctx, 1, hiddenStates.Dim(1)-1, hiddenStates.Dim(1), 1)
 	default:
 		panic("unknown pooling type")
 	}

model/models/bert/embed.go

@@ -29,7 +29,7 @@ type Model struct {
 // Forward implements model.Model.
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
-	hiddenStates = hiddenStates.Add(ctx, m.TypeEmbedding.Weight.View(ctx, 0, m.hiddenSize))
+	hiddenStates = hiddenStates.Add(ctx, m.TypeEmbedding.Weight.Slice(ctx, 1, 0, 1, 1))
 	hiddenStates = hiddenStates.Add(ctx, m.PositionEmbedding.Forward(ctx, ctx.Input().FromInts(batch.Positions, len(batch.Positions))))
 	hiddenStates = m.TokenEmbeddingNorm.Forward(ctx, hiddenStates, m.eps)
 

model/models/deepseek2/model.go

@@ -78,44 +78,31 @@ func (attn *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor
 	}
 
 	query = query.Reshape(ctx, query.Dim(0)/opts.numHeads, opts.numHeads, seqLength)
-
-	qPass := query.View(ctx, 0,
-		opts.qkNopeHeadDim, query.Stride(1),
-		query.Dim(1), query.Stride(2),
-		query.Dim(2))
-
-	qRot := query.View(ctx, opts.qkNopeHeadDim*query.Stride(0),
-		opts.qkRopeHeadDim, query.Stride(1),
-		query.Dim(1), query.Stride(2),
-		query.Dim(2))
+	queryChunks := query.ChunkSections(ctx, 0, opts.qkNopeHeadDim, opts.qkRopeHeadDim)
 
 	compressedKV := attn.KVA.Forward(ctx, hiddenStates)
-
-	kPass := compressedKV.View(ctx, 0, opts.kvLoraRank, compressedKV.Stride(1), compressedKV.Dim(1))
-	kRot := compressedKV.View(ctx, opts.kvLoraRank*compressedKV.Stride(0),
-		opts.qkRopeHeadDim, compressedKV.Stride(1),
-		1, compressedKV.Stride(1),
-		compressedKV.Dim(1))
+	kPass := compressedKV.Slice(ctx, 0, 0, opts.kvLoraRank, 1)
+	kRot := compressedKV.View(ctx,
+		opts.kvLoraRank*compressedKV.Stride(0), opts.qkRopeHeadDim,
+		compressedKV.Stride(1), 1,
+		compressedKV.Stride(1), compressedKV.Dim(1),
+	)
 
 	kPass = attn.KVANorm.Forward(ctx, kPass, opts.eps)
 	kPass = attn.KVB.Forward(ctx, kPass)
 
 	kv := kPass.Reshape(ctx, kPass.Dim(0)/opts.numKVHeads, opts.numKVHeads, seqLength)
-	kPass = kv.View(ctx, 0, opts.kqNopeHeadDim, kv.Stride(1), kv.Dim(1), kv.Stride(2), kv.Dim(2))
-	value := kv.View(ctx, opts.kqNopeHeadDim*kv.Stride(0),
-		opts.vHeadDim, kv.Stride(1),
-		kv.Dim(1), kv.Stride(2),
-		kv.Dim(2)).Contiguous(ctx)
+	kvChunks := kv.ChunkSections(ctx, 0, opts.kqNopeHeadDim, opts.vHeadDim)
 
-	qRot = fast.RoPE(ctx, qRot, positions, opts.qkRopeHeadDim, opts.ropeBase, 1./opts.ropeScale, opts.RoPEOptions()...)
+	qRot := fast.RoPE(ctx, queryChunks[1], positions, opts.qkRopeHeadDim, opts.ropeBase, 1./opts.ropeScale, opts.RoPEOptions()...)
 	kRot = fast.RoPE(ctx, kRot, positions, opts.qkRopeHeadDim, opts.ropeBase, 1./opts.ropeScale, opts.RoPEOptions()...)
 
-	kRot = kRot.Repeat(ctx, 1, qPass.Dim(1))
+	kRot = kRot.Repeat(ctx, 1, queryChunks[0].Dim(1))
 
-	query = qRot.Concat(ctx, qPass, 0)
-	key := kRot.Concat(ctx, kPass, 0)
+	query = qRot.Concat(ctx, queryChunks[0], 0)
+	key := kRot.Concat(ctx, kvChunks[0], 0)
 
-	attention := nn.Attention(ctx, query, key, value, opts.kqScale, cache)
+	attention := nn.Attention(ctx, query, key, kvChunks[1], opts.kqScale, cache)
 	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), seqLength)
 	return attn.Output.Forward(ctx, attention)
 }
@@ -142,6 +129,7 @@ func (moe *sparse) Moe(ctx ml.Context, hiddenStates, topKIndices, topKWeights ml
 
 	experts := moe.Down.Weight.MulmatID(ctx, hiddenStates, topKIndices)
 	experts = experts.Mul(ctx, topKWeights)
+
 	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
 	for i := 1; i < opts.numExpertsUsed; i++ {
 		nextStates = nextStates.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))

model/models/gemma3n/model_text.go

@@ -64,18 +64,18 @@ func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cac
 
 		cache.(*kvcache.WrapperCache).SetLayerType(layerType)
 
-		// inputPerLayer = inputsPerLayer[:, i, :]
-		inputPerLayer := inputsPerLayer.View(ctx, i*inputsPerLayer.Stride(1), inputsPerLayer.Dim(0), inputsPerLayer.Stride(2), inputsPerLayer.Dim(2)).Contiguous(ctx)
+		// inputPerLayer = inputsPerLayer[:, i, :].squeeze(1)
+		inputPerLayer := inputsPerLayer.View(ctx, i*inputsPerLayer.Stride(1), inputsPerLayer.Dim(0), inputsPerLayer.Stride(2), inputsPerLayer.Dim(2))
 		hiddenStates = layer.Forward(ctx, hiddenStates, inputPerLayer, positions, one, cache, i >= firstSharedKeyValue, ropeBase, float64(m.activationSparsityScale[i]), &m.TextOptions)
 	}
 
 	// hiddenStates = hiddenStates[:, :, 0]
-	hiddenStates0 := hiddenStates.View(ctx, 0, hiddenStates.Dim(0), hiddenStates.Stride(1), hiddenStates.Dim(1))
+	hiddenStates0 := hiddenStates.Slice(ctx, 2, 0, 1, 1)
 	targetMagnitude = hiddenStates0.Sqr(ctx).Mean(ctx).Sqrt(ctx)
 	targetMagnitude = targetMagnitude.Repeat(ctx, 2, m.altupInputs-1)
 
 	// hiddenState = hiddenStates[:, :, 1:]
-	hiddenState = hiddenStates.View(ctx, hiddenStates.Stride(2), hiddenStates.Dim(0), hiddenStates.Stride(1), hiddenStates.Dim(1), hiddenStates.Stride(2), m.altupInputs-1)
+	hiddenState = hiddenStates.Slice(ctx, 2, 1, hiddenStates.Dim(2), 1)
 	altupUnembdProj := m.AltupUnembd.Forward(ctx, hiddenState)
 	altupUnembdProj = altupUnembdProj.Mul(ctx, targetMagnitude.Div(ctx, altupUnembdProj.Sqr(ctx).Mean(ctx).Sqrt(ctx)))
 
@@ -176,10 +176,10 @@ func (d TextLayer) Forward(ctx ml.Context, hiddenStates, perLayerInput, position
 	active = d.PostPerLayerNorm.Forward(ctx, active, opts.eps)
 
 	// inactive := predictions[:, :, 1:]
-	inactive := predictions.View(ctx, predictions.Stride(2), predictions.Dim(0), predictions.Stride(1), predictions.Dim(1), predictions.Stride(2), predictions.Dim(2)-1)
+	inactive := predictions.Slice(ctx, 2, 1, predictions.Dim(2), 1)
 	active = inactive.Add(ctx, active)
 
-	predictions0 := predictions.View(ctx, 0, predictions.Dim(0), predictions.Stride(1), predictions.Dim(1))
+	predictions0 := predictions.Slice(ctx, 2, 0, 1, 1)
 	return predictions0.Concat(ctx, active, 2)
 }
 
@@ -319,7 +319,7 @@ type TextOptions struct {
 
 func (o *TextOptions) altupActive(ctx ml.Context, t ml.Tensor) ml.Tensor {
 	// t[:, :, o.altupActiveIndex]
-	return t.View(ctx, o.altupActiveIndex*t.Stride(2), t.Dim(0), t.Stride(1), t.Dim(1))
+	return t.Slice(ctx, 2, o.altupActiveIndex, o.altupActiveIndex+1, 1)
 }
 
 func (o *TextOptions) headDim() int {

model/models/gptoss/model.go

@@ -121,30 +121,9 @@ func (attn *AttentionBlock) Forward(ctx ml.Context, hiddenStates, positions ml.T
 	var query, key, value ml.Tensor
 	if attn.QKV != nil {
 		qkv := attn.QKV.Forward(ctx, hiddenStates)
-
-		// query = qkv[..., : num_attention_heads * head_dim].reshape(batch_size, num_attention_heads, head_dim)
-		query = qkv.View(ctx,
-			0,
-			opts.headDim(), qkv.Stride(0)*opts.headDim(),
-			opts.numHeads, qkv.Stride(1),
-			batchSize,
-		)
-
-		// key = qkv[..., num_attention_heads * head_dim:(num_attention_heads + num_key_value_heads) * head_dim].reshape(batch_size, num_key_value_heads, head_dim)
-		key = qkv.View(ctx,
-			qkv.Stride(0)*opts.headDim()*opts.numHeads,
-			opts.headDim(), qkv.Stride(0)*opts.headDim(),
-			opts.numKVHeads, qkv.Stride(1),
-			batchSize,
-		)
-
-		// value = qkv[..., (num_attention_heads  + num_key_value_heads) * head_dim:].reshape(batch_size, num_key_value_heads, head_dim)
-		value = qkv.View(ctx,
-			qkv.Stride(0)*opts.headDim()*(opts.numHeads+opts.numKVHeads),
-			opts.headDim(), qkv.Stride(0)*opts.headDim(),
-			opts.numKVHeads, qkv.Stride(1),
-			batchSize,
-		)
+		qkv = qkv.Reshape(ctx, opts.headDim(), -1, batchSize)
+		chunks := qkv.ChunkSections(ctx, 1, opts.numHeads, opts.numKVHeads, opts.numKVHeads)
+		query, key, value = chunks[0], chunks[1], chunks[2]
 	} else {
 		query = attn.Query.Forward(ctx, hiddenStates)
 		query = query.Reshape(ctx, opts.headDim(), opts.numHeads, batchSize)
@@ -195,15 +174,8 @@ func (mlp *MLPBlock) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Optio
 	var gate, up ml.Tensor
 	if mlp.GateUp != nil {
 		hiddenStates = mlp.GateUp.Forward(ctx, hiddenStates, selectedExperts)
-		hiddenStates = hiddenStates.Reshape(ctx, 2, hiddenStates.Dim(0)/2, hiddenStates.Dim(1), hiddenStates.Dim(2))
-
-		dimStride := []int{hiddenStates.Dim(0) / 2, hiddenStates.Stride(1), hiddenStates.Dim(1), hiddenStates.Stride(2), hiddenStates.Dim(2), hiddenStates.Stride(3), hiddenStates.Dim(3)}
-
-		gate = hiddenStates.View(ctx, 0, dimStride...)
-		gate = gate.Contiguous(ctx, gate.Dim(0)*gate.Dim(1), gate.Dim(2), gate.Dim(3))
-
-		up = hiddenStates.View(ctx, hiddenStates.Stride(0), dimStride...)
-		up = up.Contiguous(ctx, up.Dim(0)*up.Dim(1), up.Dim(2), up.Dim(3))
+		gate = hiddenStates.Slice(ctx, 0, 0, hiddenStates.Dim(0), 2)
+		up = hiddenStates.Slice(ctx, 0, 1, hiddenStates.Dim(0), 2)
 	} else {
 		gate = mlp.Gate.Forward(ctx, hiddenStates, selectedExperts)
 		up = mlp.Up.Forward(ctx, hiddenStates, selectedExperts)

model/models/llama4/model.go

@@ -105,9 +105,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 
 		for range aspectRatio.Y {
 			for x := range aspectRatio.X {
-				view := projectedOutputs.View(ctx, projectedOutputs.Stride(1)*offset,
-					projectedOutputs.Dim(0), projectedOutputs.Stride(1),
-					patchesPerChunk)
+				view := projectedOutputs.Slice(ctx, 1, offset, offset+patchesPerChunk, 1)
 				var separator separator
 				if x < aspectRatio.X-1 {
 					separator.x = true // <|tile_x_separator|>
@@ -120,9 +118,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 		}
 	}
 
-	view := projectedOutputs.View(ctx, projectedOutputs.Stride(1)*offset,
-		projectedOutputs.Dim(0), projectedOutputs.Stride(1),
-		patchesPerChunk)
+	view := projectedOutputs.Slice(ctx, 1, offset, offset+patchesPerChunk, 1)
 	multimodal = append(multimodal, input.Multimodal{Tensor: view, Data: &separator{}})
 
 	return multimodal, nil

model/models/llama4/model_vision.go

@@ -37,27 +37,23 @@ type VisionAttention struct {
 func applyVisionRotaryEmbedding(ctx ml.Context, t, cos, sin ml.Tensor) ml.Tensor {
 	width, height, channels, tiles := t.Dim(0), t.Dim(1), t.Dim(2), t.Dim(3)
 
-	t = t.Reshape(ctx, 2, t.Dim(0)/2, t.Dim(1)*t.Dim(2)*t.Dim(3))
-
 	// t1 = t[..., 0::2]
-	t1 := t.View(ctx, 0, 1, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2)).Contiguous(ctx)
-	t1 = t1.Reshape(ctx, width/2, height, channels, tiles)
+	t1 := t.Slice(ctx, 0, 0, t.Dim(0), 2)
 
 	// t2 = t[..., 1::2]
-	t2 := t.View(ctx, t.Stride(0), 1, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2)).Contiguous(ctx)
-	t2 = t2.Reshape(ctx, width/2, height, channels, tiles)
+	t2 := t.Slice(ctx, 0, 1, t.Dim(0), 2)
 
 	// cos_out = torch.stack((t1 * cos, t2 * cos), dim=-1)
 	cosOut := t1.Mul(ctx, cos).Concat(ctx, t2.Mul(ctx, cos), 0)
-	cosOut = cosOut.Reshape(ctx, cosOut.Dim(0)/2, 2, cosOut.Dim(1)*cosOut.Dim(2)*cosOut.Dim(3))
-	cosOut = cosOut.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	cosOut = cosOut.Reshape(ctx, width, height, channels, tiles)
+	cosOut = cosOut.Reshape(ctx, cosOut.Dim(0)/2, 2, -1)
+	cosOut = cosOut.Permute(ctx, 1, 0, 2, 3)
+	cosOut = cosOut.Contiguous(ctx, width, height, channels, tiles)
 
 	// sin_out = torch.stack((-t2 * sin, t1 * sin), dim=-1)
-	sinOut := t2.Neg(ctx).Mul(ctx, sin).Concat(ctx, t1.Mul(ctx, sin), 0)
-	sinOut = sinOut.Reshape(ctx, sinOut.Dim(0)/2, 2, sinOut.Dim(1)*sinOut.Dim(2)*sinOut.Dim(3))
-	sinOut = sinOut.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	sinOut = sinOut.Reshape(ctx, width, height, channels, tiles)
+	sinOut := t2.Scale(ctx, -1).Mul(ctx, sin).Concat(ctx, t1.Mul(ctx, sin), 0)
+	sinOut = sinOut.Reshape(ctx, sinOut.Dim(0)/2, 2, -1)
+	sinOut = sinOut.Permute(ctx, 1, 0, 2, 3)
+	sinOut = sinOut.Contiguous(ctx, width, height, channels, tiles)
 
 	return cosOut.Add(ctx, sinOut)
 }

model/models/mistral3/model_vision.go

@@ -11,9 +11,9 @@ import (
 var batchSize int = 1
 
 func rotateHalf(ctx ml.Context, t ml.Tensor) ml.Tensor {
-	x1 := t.View(ctx, 0, t.Dim(0)/2, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2), t.Stride(3), t.Dim(3))
-	x2 := t.View(ctx, t.Stride(0)*t.Dim(0)/2, t.Dim(0)/2, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2), t.Stride(3), t.Dim(3)).Contiguous(ctx)
-	return x2.Neg(ctx).Concat(ctx, x1, 0)
+	x1 := t.Slice(ctx, 0, 0, t.Dim(0)/2, 1)
+	x2 := t.Slice(ctx, 0, t.Dim(0)/2, t.Dim(0), 1).Contiguous(ctx)
+	return x2.Scale(ctx, -1).Concat(ctx, x1, 0)
 }
 
 func applyRotaryPositionalEmbedding(ctx ml.Context, t, cos, sin ml.Tensor) ml.Tensor {

model/models/qwen25vl/model_vision.go

@@ -13,9 +13,9 @@ import (
 var batchSize int = 1
 
 func rotateHalf(ctx ml.Context, t ml.Tensor) ml.Tensor {
-	x1 := t.View(ctx, 0, t.Dim(0)/2, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2), t.Stride(3), t.Dim(3))
-	x2 := t.View(ctx, t.Stride(0)*t.Dim(0)/2, t.Dim(0)/2, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2), t.Stride(3), t.Dim(3)).Contiguous(ctx)
-	return x2.Neg(ctx).Concat(ctx, x1, 0)
+	x1 := t.Slice(ctx, 0, 0, t.Dim(0)/2, 1)
+	x2 := t.Slice(ctx, 0, t.Dim(0)/2, t.Dim(0), 1).Contiguous(ctx)
+	return x2.Scale(ctx, -1).Concat(ctx, x1, 0)
 }
 
 func applyRotaryPositionalEmbedding(ctx ml.Context, t, cos, sin ml.Tensor) ml.Tensor {

model/models/qwen3vl/model_vision.go

@@ -18,8 +18,8 @@ type VisionAttention struct {
 }
 
 func rotateHalf(ctx ml.Context, t ml.Tensor) ml.Tensor {
-	x1 := t.View(ctx, 0, t.Dim(0)/2, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2), t.Stride(3), t.Dim(3))
-	x2 := t.View(ctx, t.Stride(0)*t.Dim(0)/2, t.Dim(0)/2, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2), t.Stride(3), t.Dim(3)).Contiguous(ctx)
+	x1 := t.Slice(ctx, 0, 0, t.Dim(0)/2, 1)
+	x2 := t.Slice(ctx, 0, t.Dim(0)/2, t.Dim(0), 1).Contiguous(ctx)
 	return x2.Scale(ctx, -1).Concat(ctx, x1, 0)
 }
 
@@ -160,10 +160,11 @@ func (m *VisionPositionEmbedding) Forward(ctx ml.Context, hiddenStates ml.Tensor
 	positionEmbeds = positionEmbeds.Mul(ctx, weights)
 	positionEmbeds = positionEmbeds.Reshape(ctx, n, -1, 4)
 
-	positionEmbeds = positionEmbeds.View(ctx, 0, n, positionEmbeds.Stride(1), grid.Height*grid.Width).
-		Add(ctx, positionEmbeds.View(ctx, 1*positionEmbeds.Stride(2), n, positionEmbeds.Stride(1), grid.Height*grid.Width)).
-		Add(ctx, positionEmbeds.View(ctx, 2*positionEmbeds.Stride(2), n, positionEmbeds.Stride(1), grid.Height*grid.Width)).
-		Add(ctx, positionEmbeds.View(ctx, 3*positionEmbeds.Stride(2), n, positionEmbeds.Stride(1), grid.Height*grid.Width))
+	positionEmbedsChunks := positionEmbeds.Chunk(ctx, 2, 1)
+	positionEmbeds = positionEmbedsChunks[0].
+		Add(ctx, positionEmbedsChunks[1]).
+		Add(ctx, positionEmbedsChunks[2]).
+		Add(ctx, positionEmbedsChunks[3])
 
 	positionEmbeds = positionEmbeds.Reshape(ctx, -1, grid.Width/opts.spatialMergeSize, opts.spatialMergeSize, grid.Height/opts.spatialMergeSize)
 	positionEmbeds = positionEmbeds.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, n, -1)